Image processing apparatus and its method, program, and storage medium

ABSTRACT

An object of this invention is to efficiently attain noise removal. In order to achieve the object, the discrete wavelet transforms of an input image are computed to output wavelet coefficients of respective subbands (S 301 ). Appropriate threshold values are respectively set for subbands HL, LH, and HH indicating high-frequency components (S 302   a , S 302   b , S 302   c ). The wavelet coefficients of the subbands HL, LH, and HH then undergo threshold value processes using the set threshold values for the respective subbands (S 303   a , S 303   b , S 303   c ). Pixels to be processed in a coefficient conversion process are determined based on the threshold value processing results of the respective subbands (S 304 ). The wavelet coefficients of the respective subbands corresponding to the pixels to be processed determined in step S 304  undergo coefficient conversion (S 305 ), and the converted transformation coefficients undergo inverse discrete wavelet transformation, thus reconstructing and outputting a noise-removed image (S 306 ).

FIELD OF THE INVENTION

The present invention relates to an image processing apparatus forchanging an image and, more particularly, changing transformationcoefficients upon frequency transformation, its method, a program, and astorage medium.

BACKGROUND OF THE INVENTION

With the recent advance of digital technologies, a radiation image isconverted into a digital image signal, and that digital image signalundergoes an image process, thus displaying the processed signal on,e.g., a CRT or printing it out. Upon photographing a radiation image,the X-ray dose is preferably as small as possible in consideration ofthe influences on a patient. But an image sensed with a small X-ray dosecontains many quantization noise components, which disturb diagnosis.

For this reason, processes for removing such noise have beenconventionally examined. For example, a noise removal process using asimple median filter, a method of removing noise by extractinghigh-frequency components using a smoothed image, and the like are done.In recent years, a multiplex frequency process for removing noise bysegmenting an input image into a plurality of frequency bands, andexecuting independent processes for respective frequency bands has beenexamined.

In a filter process for removing noise by extracting high-frequencycomponents using a smoothed image, since a single frequency band isused, noise removal cannot be effectively made if noise components aredistributed to a broad frequency band. To avoid this, a plurality offilters having different sizes (i.e., different frequencies) may besimultaneously used. However, computation cost required for the processincreases considerably. In order to maintain optimal frequencycharacteristics of a filter for noise removal, filter size adjustmentcorresponding to an object is indispensable, resulting in poorversatility.

The above problems can be greatly reduced using the multiplex frequencyprocess represented by, e.g., discrete wavelet transformation in noiseremoval. However, since the multiplex frequency process executes uniformfrequency processes in all spatial directions, for example, it cannotdistinguish an edge having a direction component from isolated pointnoise having no direction component, and it is difficult for thatprocess to remove noise while preserving the edge.

The present invention has been made in consideration of theaforementioned problems, and has as its object to effectively executenoise removal.

SUMMARY OF THE INVENTION

In order to achieve the above object, for example, an image processingapparatus of the present invention comprises the following arrangement.

That is, an image processing apparatus comprises:

frequency transformation means for obtaining a plurality of subbands bycomputing frequency transforms of an image;

determination means for determining transformation coefficients in asubband to be changed using at least two subbands of the plurality ofsubbands obtained by the frequency transformation means; and

coefficient conversion means for converting the transformationcoefficients determined by the determination means,

wherein the frequency transformation means reconstructs an image usingtransformation coefficients of all the subbands including thetransformation coefficients converted by the coefficient conversionmeans.

The determination means further comprises:

threshold value setting means for setting threshold values for at leasttwo subbands of the plurality of subbands obtained by the frequencytransformation means; and

threshold value processing means for executing a threshold value processof transformation coefficients in the subbands for which the thresholdvalues are set by the threshold value setting means, using the thresholdvalues, and

the determination means determines the transformation coefficients inthe subband to be changed using a threshold value processing result ofthe threshold value processing means.

Other features and advantages of the present invention will be apparentfrom the following description taken in conjunction with theaccompanying drawings, in which like reference characters designate thesame or similar parts throughout the figures thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention and,together with the description, serve to explain the principles of theinvention.

FIG. 1 is a block diagram showing the basic arrangement of an X-rayphotographing apparatus according to the first embodiment of the presentinvention, and an object to be photographed using X-rays;

FIG. 2 is a flow chart showing an outline of processes done by the X-rayphotographing apparatus according to the first embodiment of the presentinvention;

FIG. 3 is a flow chart showing the flow of processes that pertain tonoise removal in an image processing circuit 112;

FIGS. 4A to 4D show the states of processes according to the flow chartshown in FIG. 3;

FIG. 5A is a diagram showing the arrangement of the process of adiscrete wavelet transformation circuit 116;

FIG. 5B shows an example of the format of transformation coefficients oftwo levels obtained by a two-dimensional transformation process; and

FIG. 5C is a diagram showing the arrangement of an inverse discretewavelet transformation process by the discrete wavelet transformationcircuit 116.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Preferred embodiments of the present invention will now be described indetail in accordance with the accompanying drawings.

First Embodiment

FIG. 1 shows the basic arrangement of an X-ray photographing apparatus100 of this embodiment, and an object to be photographed using X-rays.An X-ray photographing apparatus 100 has a function of photographing animage of an object using X-rays, and removing noise from thephotographed image, and comprises a pre-processing circuit 106, CPU 108,main memory 109, control panel 110, image display 111, and imageprocessing circuit 112. Note that the image processing circuit 112comprises a discrete wavelet transformation (DWT) circuit 116,coefficient conversion circuit 117, feature extraction circuit 118, toneconversion circuit 119, threshold value processing circuit 120, andprocessing discrimination circuit 121, which exchange data via a CPU bus107.

The X-ray photographing apparatus 100 comprises a data acquisitioncircuit 105 connected to the pre-processing circuit 106, and atwo-dimensional X-ray sensor 104 and X-ray generation circuit 101, whichare connected to the data acquisition circuit 105, and these circuitsare also connected to the CPU bus 107.

FIG. 2 is a flow chart showing an outline of processes executed by theX-ray photographing apparatus 100 of this embodiment with the abovearrangement. Note that the main memory 109 stores various data and thelike required for processes executed by the CPU 108, and includes a workarea for the CPU 108. The CPU 108 controls the operations of the overallapparatus using the main memory 109 in accordance with operations at thecontrol panel 110. With this control, the X-ray photographing apparatus100 operates as follows.

The X-ray generation circuit 101 generates an X-ray beam 102 toward anobject 103. The X-ray beam 102 generated by the X-ray generation circuit101 is transmitted through the object 103 while being attenuated, andreaches the two-dimensional X-ray sensor 104, which outputs the receivedbeam as an X-ray image (step S201). In this embodiment, an X-ray imageoutput from the two-dimensional X-ray sensor 104 is a human body image,but the present invention is not limited to such specific image.

The data acquisition circuit 105 converts an X-ray image output from thetwo-dimensional X-ray sensor 104 into an electrical signal, and suppliesthe electrical signal to the pre-processing circuit 106. Thepre-processing circuit 106 executes pre-processes such as an offsetcorrection process, gain correction process, and the like for the signal(X-ray image signal) output from the data acquisition circuit 105 (stepS202). The X-ray image signal that has undergone the pre-processes bythe pre-processing circuit 106 is transferred as a source image to themain memory 109 and image processing circuit 112 via the CPU bus 107under the control of the CPU 108, and then undergoes a noise removalprocess (step S203), as will be described later.

In the image processing circuit 112, the discrete wavelet transformationcircuit 116 computes the discrete wavelet transforms of the input image,and outputs wavelet coefficients for respective subbands. The thresholdvalue processing circuit 120 sets appropriate threshold values forrespective subbands, and executes threshold value processes of discretewavelet coefficients using the set threshold values. The processingdiscrimination circuit 121 discriminates based on the threshold valueprocessing results of the respective subbands if discrete waveletcoefficients of respective pixels are to be processed. The coefficientconversion circuit 117 converts discrete wavelet coefficients accordingto given rules (to be described later). The image processing circuit 112also comprises the feature extraction circuit 118 for extracting afeature amount required for tone conversion, and the tone conversioncircuit 119 for making tone conversion in accordance with the extractedfeature amount.

FIG. 3 is a flow chart showing the flow of processes that pertain tonoise removal in the image processing circuit 112, and FIGS. 4A to 4Dshow the states of the processes. The flow of a noise removal processwill be explained below using these figures.

The source image (image 401 shown in FIG. 4A), which has undergone thepre-processes by the pre-processing circuit 106, is transferred to theimage processing circuit 112 via the CPU bus 107. In the imageprocessing circuit 112, the discrete wavelet transformation circuit 116computes the discrete wavelet transforms of the input image, and outputsdiscrete wavelet coefficients for respective subbands (step S301). Asdescribed above, reference numeral 401 in FIG. 4A denotes an inputimage; and 402 in FIG. 4B, an image that has undergone discrete wavelettransformation for one level. In the image 402, regions 403 to 406 aresubbands generally called LL, HL, LH, and HH, respectively. Details ofthe discrete wavelet transformation will be explained later.

An appropriate threshold value is set for each of three subbands HL, LH,and HH indicating high-frequency components of those obtained in stepS301 (steps S302 a, 302 b, and S302 c). The setting method of thethreshold values is not particularly limited. For example, givenconstants obtained by experience may be set in correspondence with thedecomposition levels of discrete wavelet transformation, or thethreshold values may be obtained based on statistical quantities such asaverages, variances, and the like of coefficient values of therespective subbands.

Using the threshold values of the respective subbands set in steps S302a, S302 b, and S302 c, wavelet coefficients of the subbands HL, LH, andHH undergo threshold value processes (steps S303 a, S303 b, and S303 c).In FIG. 4C, reference numeral 407 denotes an image after discretewavelet transformation obtained in step S301; and 408, an image that hasundergone the threshold value processes in steps S303 a, S303 b, andS303 c. At this time, the images 407 and 408 are held in differentmemory spaces. As a method of the threshold value process, for example,a signed binarization process may be used, and can be implemented asfollows.

if (−THHL<HL(x,y)<THHL)

then binary image HL(x,y)=1;

else binary image HL(x,y)=0;

if (−THLH<LH(x,y)<THLH)

then binary image LH(x,y)=1;

else binary image LH(x,y)=0;

if (−THHH<HH(x,y)<THHH)

then binary image HH(x,y)=1;

else binary image HH(x,y)=0;

where THHL, THLH, and THHH are the threshold values of the respectivesubbands set in steps S302 a, S302 b, and S302 c, and HL(x,y), LH(x,y),and HH(x,y) are pixel values at positions (x,y) in the respectivesubbands, i.e., wavelet coefficient values.

Based on the threshold value processing results of the respectivesubbands in steps S303 a, S303 b, and S303 c, pixels to be processed inthe subsequent coefficient conversion process are determined (stepS304). Some determination methods of the pixels to be processed may beproposed. In this embodiment, the AND of binary images of the respectivesubbands obtained in steps S303 a, S303 b, and S303 c is adopted. Thatis, the pixel to be processed is determined as follows.

if ((binary image HL(x,y)=1) AND

(binary image LH(x,y)=1) AND

(binary image HH(x,y)=1))

then binary image HL(x,y)=binary image LH(x,y)=binary image HH(x,y)=1;

else binary image HL(x,y)=binary image LH(x,y)=binary image HH(x,y)=0;

In FIG. 4D, reference numeral 409 denotes an image after discretewavelet transformation obtained in step S301; and 410, an imageindicating the pixels to be processed determined in step S304. At thistime, the image 410 may be held in the same memory space as that whichstores the image 408 shown in FIG. 4C. As another determination methodof the pixels to be processed, a method of selecting a majority of thethreshold value processing results of the respective subbands may beused.

The coefficient conversion circuit 117 executes coefficient conversionof wavelet coefficients of the respective subbands, which correspond tothe pixels to be processed determined in step S304 (step S305). As anexample of the process in step S305, a well-known noise removal processcalled wavelet degeneration may be used. Wavelet degeneration is thatcoefficients of respective subbands are suppressed based on a givencondition. As a simplest example, when the absolute value of acoefficient value is present within a given range, a process forreplacing that coefficient by 0 is done:

if (TH1≦|HH(x,y)|≦TH2) then HH(x,y)=0; else DO NOTHING

if (TH3≦|HL(x,y)|≦TH4) then HL(x,y)=0; else DO NOTHING

if (TH5≦|LH(x,y)|≦TH6) then LH(x,y)=0; else DO NOTHING

where TH1 to TH6 are threshold values determined independently, and areprocessing parameters which satisfy:

0≦TH1≦TH2

 0≦TH3≦TH4

0≦TH5≦TH6

Finally, using the wavelet coefficients of the respective subbands thathave been converted by the coefficient conversion circuit 117 in stepS305, the discrete wavelet transformation circuit 116 computes theinverse discrete wavelet transforms to reclaim and output anoise-removed image (step S306).

In the noise removal process of this embodiment that has been explainedusing FIG. 3 and FIGS. 4A to 4D, discrete wavelet transformation of onelevel is done. However, the present invention is not limited to this.That is, the same processes can be done for wavelet coefficients of ahigher level, and also for wavelet coefficients of a plurality oflevels.

The discrete wavelet transformation and inverse discrete wavelettransformation of the discrete wavelet transformation circuit 116 arewell-known transformation processes, and operate as follows.

The discrete wavelet transformation circuit 116 executes atwo-dimensional discrete wavelet transformation process for an inputimage signal, and computes and outputs transformation coefficients.Image data stored in the main memory 109 is sequentially read out,undergoes transformation, and is written again in the main memory 109 bythe discrete wavelet transformation circuit 116. Assume that thearrangement of the process of the discrete wavelet transformationcircuit 116 in this embodiment is as shown in FIG. 5A. In FIG. 5A, aninput image signal is separated into odd and even address signals by acombination of a delay element and down samplers, and undergoes filterprocesses of two filters p and u. In FIG. 5A, s and d represent low- andhigh-pass coefficients upon decomposing a linear image signal to onelevel, and are respectively computed by:

d(n)=x(2n+1)−floor((x(2n)+x(2n+2))/2)  (1)

s(n)=x(2n)+floor((d(n−1)+d(n))/4)  (2)

where x(n) is an image signal to be transformed. With the above process,a linear discrete wavelet transformation process is done for an imagesignal. Since two-dimensional discrete wavelet transformation isimplemented by sequentially executing linear discrete wavelettransformation in the horizontal and vertical directions of an image andits details are known to those who are skilled in the art, a descriptionthereof will be omitted. FIG. 5B shows an example of the format oftransformation coefficient groups of two levels obtained by thetwo-dimensional discrete wavelet transformation process. An image signalis decomposed into coefficient sequences HH1, HL1, LH1, . . . , HH2,HL2, LH2, and LL in different frequency bands.

Likewise, inverse discrete wavelet transformation is done as follows.The transformation coefficients stored in the main memory 109 aresequentially read out, undergo transformation, and are written again inthe main memory 109 by the discrete wavelet transformation circuit 116.Assume that the arrangement of the inverse discrete wavelettransformation of the discrete wavelet transformation circuit 116 inthis embodiment is as shown in FIG. 5C. Input transformationcoefficients undergo filter processes using two filters u and p, and areadded to each other after being up-sampled, thus outputting an imagesignal x′. These processes are described by:

x′(2n)=s′(n)−floor((d′(n−1)+d′(n))/4)  (3)

x′(2n+1)=d′(n)+floor((x′(2n)+x′(2n+2))/2)  (4)

With the above process, linear inverse discrete wavelet transformationof transformation coefficients is done. Since two-dimensional inversediscrete wavelet transformation is implemented by sequentially executinglinear inverse transformation in the horizontal and vertical directionsof an image and its details are known to those who are skilled in theart, a description thereof will be omitted.

As described above, according to this embodiment, since a multiplexfrequency process is implemented by discrete wavelet transformation,even when noise components are distributed to a broad frequency band,noise can be effectively removed compared to a so-called filter processthat uses a single frequency band. Also, the computation cost requiredfor the process can be greatly reduced compared to a process thatsimultaneously uses a plurality of filters having different sizes (i.e.,different frequencies), and a process with high versatility can be donewithout complicated processes such as filter size adjustment and thelike. Since the multiplex frequency process is done in consideration ofthe spatial direction of high-frequency components, for example, an edgehaving a direction component can be distinguished from isolated pointnoise having no direction component. As a result, a noise-removed imagewith higher quality than the conventional method can be obtained.

Second Embodiment

In steps S302 a to S304 of the first embodiment, the threshold valueprocesses for determining the pixels to be processed in step S305 aredone. However, the present invention is not limited to such specificprocesses. In this embodiment, three subbands are compared, and thepixels to be processed in step S305 are determined according to apattern of the comparison results. That is, by exploiting the fact thatmore random noise components appear in the subband HH, the processes insteps S302 a to S304 may be modified as:

if ((HL(x,y)<HH(x,y)) AND (LH(x,y)<HH(x,y))

then binary image HL(x,y)=binary image LH(x,y)=binary image HH(x,y)=1;

else binary image HL(x,y)=binary image LH(x,y)=binary image HH(x,y)=0;

By combining the threshold value processes and comparison, the processesin steps S302 a to S304 may be modified as follows to achieve the sameobject:

if (−THHH<HH(x,y)<THHH)

then binary image HH(x,y)=1;

else binary image HH(x,y)=0;

if ((HL(x,y)<HH(x,y)) AND

(LH(x,y)<HH(x,y)) AND

(binary image HH(x,y)=1))

then binary image HL(x,y)=binary image LH(x,y)=binary image HH(x,y)=1;

else binary image HL(x,y)=binary image LH(x,y)=binary image HH(x,y)=0;

Furthermore, the processes in steps S302 a to S304 may adopt weightedevaluation based on distances from the threshold values, or a method ofexecuting threshold value processes of the averages of respectivesubbands, and the like. A cross-reference method of the coefficients ofthe respective subbands is not particularly limited.

Another Embodiment

Note that the present invention may be applied to either a systemconstituted by a plurality of devices (e.g., a host computer, interfacedevice, reader, printer, and the like), or an apparatus consisting of asingle equipment (e.g., a copying machine, facsimile apparatus, or thelike).

The objects of the present invention are also achieved by supplying astorage medium (or recording medium), which records a program code of asoftware program that can implement the functions of the above-mentionedembodiments to the system or apparatus, and reading out and executingthe program code stored in the storage medium by a computer (or a CPU orMPU) of the system or apparatus. In this case, the program code itselfread out from the storage medium implements the functions of theabove-mentioned embodiments, and the storage medium which stores theprogram code constitutes the present invention. The functions of theabove-mentioned embodiments may be implemented not only by executing thereadout program code by the computer but also by some or all of actualprocessing operations executed by an operating system (OS) running onthe computer on the basis of an instruction of the program code.

Furthermore, the functions of the above-mentioned embodiments may beimplemented by some or all of actual processing operations executed by aCPU or the like arranged in a function extension card or a functionextension unit, which is inserted in or connected to the computer, afterthe program code read out from the storage medium is written in a memoryof the extension card or unit.

When the present invention is applied to the storage medium, thatstorage medium stores program codes corresponding to the aforementionedflow chart(s) (shown in FIG. 2 and/or FIG. 3). As described above,according to the present invention, noise removal can be efficientlydone.

The present invention is not limited to the above embodiments andvarious changes and modifications can be made within the spirit andscope of the present invention. Therefore to apprise the public of thescope of the present invention, the following claims are made.

What is claimed is:
 1. An image processing apparatus comprising: frequency transformation means for obtaining a plurality of subbands by computing frequency transforms of an image; determination means for determining transformation coefficients in a subband to be changed using at least two subbands of the plurality of subbands obtained by said frequency transformation means; and coefficient conversion means for converting the transformation coefficients determined by said determination means, wherein said frequency transformation means reconstructs an image using transformation coefficients of all the subbands including the transformation coefficients converted by said coefficient conversion means.
 2. The image processing apparatus according to claim 1, wherein said determination means further comprises: threshold value setting means for setting threshold values for at least two subbands of the plurality of subbands obtained by said frequency transformation means; and threshold value processing means for executing a threshold value process of transformation coefficients in the subbands for which the threshold values are set by said threshold value setting means, using the threshold values, and said determination means determines the transformation coefficients in the subband to be changed using a threshold value processing result of said threshold value processing means.
 3. The image processing apparatus according to claim 2, wherein said threshold value setting means sets said threshold values on the basis of statistical quantities including averages or variances of transformation coefficient values in the respective subbands.
 4. The image processing apparatus according to claim 2, wherein said threshold value setting means sets transformation coefficients in a predetermined subband as threshold values, and said threshold value processing means executes the threshold value process of the transformation coefficients in the subbands for which said threshold values are set, using the transformation coefficients in said predetermined subband, which spatially correspond to those transformation coefficients.
 5. The apparatus according to claim 4, wherein the transformation coefficients in the predetermined subband have undergone a threshold value process using a predetermined threshold value.
 6. The image processing apparatus according to claim 2, wherein said threshold value process is a binarization process.
 7. The image processing apparatus according to claim 1, wherein said coefficient conversion means implements noise removal by wavelet degeneration.
 8. The image processing apparatus according to claim 1, wherein said frequency transformation means executes one of discrete wavelet transformation and inverse discrete wavelet transformation.
 9. An image processing method comprising: the frequency transformation step of obtaining a plurality of subbands by computing frequency transforms of an image; the determination step of determining transformation coefficients in a subband to be changed using at least two subbands of the plurality of subbands obtained in the frequency transformation step; and the coefficient conversion step of converting the transformation coefficients determined in the determination step, wherein the frequency transformation step includes the step of reconstructing an image using transformation coefficients of all the subbands including the transformation coefficients converted in said coefficient conversion step.
 10. The image processing method according to claim 9, wherein said determination step further comprises: the threshold value setting step of setting threshold values for at least two subbands of the plurality of subbands obtained in said frequency transformation step; and the threshold value processing step of executing a threshold value process of transformation coefficients in the subbands for which the threshold values are set in said threshold value setting step, using the threshold values, and the determination step includes the step of determining the transformation coefficients in the subband to be changed using a threshold value processing result of said threshold value processing step.
 11. The image processing method according to claim 10, wherein said threshold value setting step includes the step of setting transformation coefficients in a predetermined subband as threshold values, and said threshold value processing step includes the step of executing the threshold value process of the transformation coefficients in the subbands for which said threshold values are set, using the transformation coefficients in said predetermined subband, which spatially correspond to those transformation coefficients.
 12. A program comprising: a program of the frequency transformation step of obtaining a plurality of subbands by computing frequency transforms of an image; a program of the determination step of determining transformation coefficients in a subband to be changed using at least two subbands of the plurality of subbands obtained in the frequency transformation step; and a program of the coefficient conversion step of converting the transformation coefficients determined in said determination step, wherein the frequency transformation step includes the step of reconstructing an image using transformation coefficients of all the subbands including the transformation coefficients converted in the coefficient conversion step.
 13. The program according to claim 12, wherein the program of the determination step comprises: a program of the threshold value setting step of setting threshold values for at least two subbands of the plurality of subbands obtained in said frequency transformation step; and a program of the threshold value processing step of executing a threshold value process of transformation coefficients in the subbands for which the threshold values are set in said threshold value setting step, using the threshold values, and the determination step includes the step of determining the transformation coefficients in the subband to be changed using a threshold value processing result of said threshold value processing step.
 14. The program according to claim 13, wherein said threshold value setting step includes the step of setting transformation coefficients in a predetermined subband as threshold values, and the threshold value processing step includes the step of executing the threshold value process of the transformation coefficients in the subbands for which said threshold values are set, using the transformation coefficients in said predetermined subband, which spatially correspond to those transformation coefficients.
 15. A computer readable storage medium that stores a program cited in claim
 12. 